


import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
						from transformers.models.mobilevitva.modeling_mobilevitva import (
						    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    make_divisible,
						)


if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.config_class(**self.inputs_dict  )
						self.parent.assertTrue(hasattr(snake_case   ,    "width_multiplier"  )  )







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=13   ,    snake_case=64   ,    snake_case=2   ,    snake_case=3   ,    snake_case="swish"   ,    snake_case=3   ,    snake_case=32   ,    snake_case=0.1   ,    snake_case=0.02   ,    snake_case=True   ,    snake_case=True   ,    snake_case=10   ,    snake_case=None   ,    snake_case=0.25   ,    snake_case=0.0   ,    snake_case=0.0   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							parent
						UpperCamelCase__       =							batch_size
						UpperCamelCase__       =							image_size
						UpperCamelCase__       =							patch_size
						UpperCamelCase__       =							num_channels
						UpperCamelCase__       =							make_divisible(512 * width_multiplier   ,    divisor=8  )
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							conv_kernel_size
						UpperCamelCase__       =							output_stride
						UpperCamelCase__       =							classifier_dropout_prob
						UpperCamelCase__       =							use_labels
						UpperCamelCase__       =							is_training
						UpperCamelCase__       =							num_labels
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							scope
						UpperCamelCase__       =							width_multiplier
						UpperCamelCase__       =							ffn_dropout
						UpperCamelCase__       =							attn_dropout




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						UpperCamelCase__       =							None
						UpperCamelCase__       =							None
						if self.use_labels:
									UpperCamelCase__       =							ids_tensor([self.batch_size]   ,    self.num_labels  )
									UpperCamelCase__       =							ids_tensor([self.batch_size, self.image_size, self.image_size]   ,    self.num_labels  )

						UpperCamelCase__       =							self.get_config()

						return config, pixel_values, labels, pixel_labels




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MobileViTVaConfig(
						    image_size=self.image_size   ,    patch_size=self.patch_size   ,    num_channels=self.num_channels   ,    hidden_act=self.hidden_act   ,    conv_kernel_size=self.conv_kernel_size   ,    output_stride=self.output_stride   ,    classifier_dropout_prob=self.classifier_dropout_prob   ,    initializer_range=self.initializer_range   ,    width_multiplier=self.width_multiplier   ,    ffn_dropout=self.ffn_dropout_prob   ,    attn_dropout=self.attn_dropout_prob   ,    )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							MobileViTVaModel(config=snake_case  )
						model.to(snake_case  )
						model.eval()
						UpperCamelCase__       =							model(snake_case  )
						self.parent.assertEqual(
						    result.last_hidden_state.shape   ,    (
						        self.batch_size,
						        self.last_hidden_size,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )   ,    )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.num_labels
						UpperCamelCase__       =							MobileViTVaForImageClassification(snake_case  )
						model.to(snake_case  )
						model.eval()
						UpperCamelCase__       =							model(snake_case   ,    labels=snake_case  )
						self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.num_labels
						UpperCamelCase__       =							MobileViTVaForSemanticSegmentation(snake_case  )
						model.to(snake_case  )
						model.eval()
						UpperCamelCase__       =							model(snake_case  )
						self.parent.assertEqual(
						    result.logits.shape   ,    (
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )   ,    )
						UpperCamelCase__       =							model(snake_case   ,    labels=snake_case  )
						self.parent.assertEqual(
						    result.logits.shape   ,    (
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.prepare_config_and_inputs()
						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							config_and_inputs
						UpperCamelCase__       =							{"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class 							lowerCamelCase__						(				UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       str 			=		(
			    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)

			_UpperCamelCase		:       Optional[int] 			=		(
			    {
			        'feature-extraction': MobileViTVaModel,
			        'image-classification': MobileViTVaForImageClassification,
			        'image-segmentation': MobileViTVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:       List[Any] 			=		False
			_UpperCamelCase		:       Dict 			=		False
			_UpperCamelCase		:       List[Any] 			=		False
			_UpperCamelCase		:       Tuple 			=		False




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							MobileViTVaModelTester(self  )
						UpperCamelCase__       =							MobileViTVaConfigTester(self   ,    config_class=snake_case   ,    has_text_modality=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			@unittest.skip(reason="MobileViTV2 does not use inputs_embeds"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="MobileViTV2 does not support input and output embeddings"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="MobileViTV2 does not output attentions"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			@require_torch_multi_gpu
			@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__       =							model_class(snake_case  )
									UpperCamelCase__       =							inspect.signature(model.forward  )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCamelCase__       =							[*signature.parameters.keys()]

									UpperCamelCase__       =							["pixel_values"]
									self.assertListEqual(arg_names[:1]   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case   ,    snake_case   ,    snake_case  ):
									UpperCamelCase__       =							model_class(snake_case  )
									model.to(snake_case  )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__       =							model(**self._prepare_for_class(snake_case   ,    snake_case  )  )

									UpperCamelCase__       =							outputs.hidden_states

									UpperCamelCase__       =							5
									self.assertEqual(len(snake_case  )   ,    snake_case  )

									# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									UpperCamelCase__       =							2
									for i in range(len(snake_case  )  ):
												self.assertListEqual(
												    list(hidden_states[i].shape[-2:]  )   ,    [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   ,    )
												divisor *= 2

									self.assertEqual(self.model_tester.output_stride   ,    divisor // 2  )

						UpperCamelCase__,   UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__       =							True
									check_hidden_states_output(snake_case   ,    snake_case   ,    snake_case  )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__       =							True

									check_hidden_states_output(snake_case   ,    snake_case   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*snake_case  )







			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__       =							MobileViTVaModel.from_pretrained(snake_case  )
									self.assertIsNotNone(snake_case  )



def 							UpperCamelCase_(					)->		Union[str, Any]:
			UpperCamelCase__       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
			return image







@require_torch
@require_vision
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return (
						    MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"  )
						    if is_vision_available()
						    else None
						)




			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"  ).to(
						    snake_case  )

						UpperCamelCase__       =							self.default_image_processor
						UpperCamelCase__       =							prepare_img()
						UpperCamelCase__       =							image_processor(images=snake_case   ,    return_tensors="pt"  ).to(snake_case  )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__       =							model(**snake_case  )

						# verify the logits
						UpperCamelCase__       =							torch.Size((1, 1000)  )
						self.assertEqual(outputs.logits.shape   ,    snake_case  )

						UpperCamelCase__       =							torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]  ).to(snake_case  )

						self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,    snake_case   ,    atol=1E-4  )  )




			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"  )
						UpperCamelCase__       =							model.to(snake_case  )

						UpperCamelCase__       =							MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"  )

						UpperCamelCase__       =							prepare_img()
						UpperCamelCase__       =							image_processor(images=snake_case   ,    return_tensors="pt"  ).to(snake_case  )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__       =							model(**snake_case  )
						UpperCamelCase__       =							outputs.logits

						# verify the logits
						UpperCamelCase__       =							torch.Size((1, 21, 32, 32)  )
						self.assertEqual(logits.shape   ,    snake_case  )

						UpperCamelCase__       =							torch.tensor(
						    [
						        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
						        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
						        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
						    ]   ,    device=snake_case   ,    )

						self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,    snake_case   ,    atol=1E-4  )  )







			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"  )
						UpperCamelCase__       =							model.to(snake_case  )

						UpperCamelCase__       =							MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"  )

						UpperCamelCase__       =							prepare_img()
						UpperCamelCase__       =							image_processor(images=snake_case   ,    return_tensors="pt"  ).to(snake_case  )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__       =							model(**snake_case  )

						UpperCamelCase__       =							outputs.logits.detach().cpu()

						UpperCamelCase__       =							image_processor.post_process_semantic_segmentation(outputs=snake_case   ,    target_sizes=[(50, 60)]  )
						UpperCamelCase__       =							torch.Size((50, 60)  )
						self.assertEqual(segmentation[0].shape   ,    snake_case  )

						UpperCamelCase__       =							image_processor.post_process_semantic_segmentation(outputs=snake_case  )
						UpperCamelCase__       =							torch.Size((32, 32)  )
						self.assertEqual(segmentation[0].shape   ,    snake_case  )






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		MvpTokenizer
			_UpperCamelCase		:       Any 			=		MvpTokenizerFast
			_UpperCamelCase		:       List[str] 			=		True
			_UpperCamelCase		:       Dict 			=		filter_roberta_detectors




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().setUp()
						UpperCamelCase__       =							[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						UpperCamelCase__       =							dict(zip(snake_case   ,    range(len(snake_case  )  )  )  )
						UpperCamelCase__       =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						UpperCamelCase__       =							{"unk_token": "<unk>"}

						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["vocab_file"]  )
						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["merges_file"]  )
						with open(self.vocab_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write(json.dumps(snake_case  ) + "\n"  )
						with open(self.merges_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write("\n".join(snake_case  )  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return "lower newer", "lower newer"




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizer.from_pretrained("RUCAIBox/mvp"  )




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						UpperCamelCase__       =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    max_length=len(snake_case  )   ,    padding=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )

									self.assertEqual((2, 9)   ,    batch.input_ids.shape  )
									self.assertEqual((2, 9)   ,    batch.attention_mask.shape  )
									UpperCamelCase__       =							batch.input_ids.tolist()[0]
									self.assertListEqual(snake_case   ,    snake_case  )
									# Test that special tokens are reset




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    padding=snake_case   ,    return_tensors="pt"  )
									# check if input_ids are returned and no labels
									self.assertIn("input_ids"   ,    snake_case  )
									self.assertIn("attention_mask"   ,    snake_case  )
									self.assertNotIn("labels"   ,    snake_case  )
									self.assertNotIn("decoder_attention_mask"   ,    snake_case  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(text_target=snake_case   ,    max_length=32   ,    padding="max_length"   ,    return_tensors="pt"  )
									self.assertEqual(32   ,    targets["input_ids"].shape[1]  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"]   ,    padding=snake_case   ,    truncation=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )
									self.assertEqual(batch.input_ids.shape   ,    (2, 1024)  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization."]
						UpperCamelCase__       =							[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    text_target=snake_case   ,    return_tensors="pt"  )
									UpperCamelCase__       =							inputs["input_ids"]
									UpperCamelCase__       =							inputs["labels"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
												UpperCamelCase__       =							self.rust_tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							self.tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							"A, <mask> AllenNLP sentence."
												UpperCamelCase__       =							tokenizer_r.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )
												UpperCamelCase__       =							tokenizer_p.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r["token_type_ids"]  )   ,    sum(tokens_p["token_type_ids"]  )  )

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )   ,    sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )   ,    )

												UpperCamelCase__       =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
												UpperCamelCase__       =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
												self.assertSequenceEqual(tokens_r["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )

												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )


